Combining Machine Learning Algorithms with Meta-Analysis and WGCNA to Identify Biomarker-Responsive Genes to Environmental Stresses in Thermus thermophilus HB۸

Introduction: Thermus thermophilus is a thermophilic bacterium known for its resilience in extreme environments. Investigating its transcriptomic responses to environmental stresses can uncover critical adaptive mechanisms. Materials and Methods: This study analyzed transcriptomic data from ۱۰ microarray datasets, including ۶۳ samples (۳۶ stress-exposed and ۲۷ controls). Stress conditions included copper, cold, zinc, iron, heat, salt, H۲O۲, tetracycline, diamide, and alkylation. Differentially expressed genes (DEGs) were identified through meta-analysis, followed by Gene Ontology (GO) enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was employed to detect stress-associated gene modules. Machine learning approaches—decision tree, logistic regression, random forest, adaptive boosting, SVM-RFE, and XGBoost—were used to prioritize key genes. Results: Meta-analysis revealed ۵۴ upregulated and ۱۹۶ downregulated genes under stress. GO analysis highlighted significant enrichment in ion transport, localization processes, and transmembrane transporter activity. WGCNA identified two stress-related modules, cyan and lightcyan. SVM-RFE and XGBoost outperformed other machine learning models with superior accuracy, precision, recall, and F۱-scores. TTHA۰۷۹۸ emerged as a hub gene consistently identified across machine learning and DEG/WGCNA analyses. Conclusions: This study provides a comprehensive analysis of the stress responses of T. thermophilus, identifying TTHA۰۷۹۸ as a key hub gene. The integration of transcriptomic data, co-expression analysis, and machine learning offers valuable insights into the adaptive mechanisms of this extremophile, paving the way for further functional studies.

کلیدواژه ها:

Bacteria ، Environmental stress ، Gene expression ، Meta-analysis ، Machine Learning ، WGCNA